Impact of low-frequency repetitive transcranial magnetic stimulation on functional network connectivity in schizophrenia patients with auditory verbal hallucinations

Xie, Yangli; Guan, Muzhen; Cai, Yun; Wang, Zhongheng; Ma, Zhengliang; Peng, Fang; Wang, Huaning

doi:10.1016/j.psychres.2022.114974

Cited by 14 publications

(5 citation statements)

References 101 publications

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“…Also, rTMS has the potential to reshape the connectivity of functionally interconnected regions by entraining endogenous brain oscillations (Thut & Miniussi, 2009 ) and thereby improving neural synchrony. Our prior investigation demonstrated that functional network connectivity is enhanced in schizophrenia patients with AVH following treatment, as determined by low‐frequency rTMS (Xie, Guan, Cai, et al, 2023 ; Xie, Guan, Wang, et al, 2023 ). Moreover, it has been observed that low‐frequency rTMS modulates the somatomotor network, which is accountable for somatic movements and sensations (Oosterwijk et al, 2012 ), thereby coordinating its structural and functional attributes.…”

Section: Discussionmentioning

confidence: 99%

Low‐frequency rTMS induces modifications in cortical structural connectivity ‐ functional connectivity coupling in schizophrenia patients with auditory verbal hallucinations

Xie,

Li,

Guan

et al. 2024

Human Brain Mapping

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Auditory verbal hallucinations (AVH) are distinctive clinical manifestations of schizophrenia. While low‐frequency repetitive transcranial magnetic stimulation (rTMS) has demonstrated potential in mitigating AVH, the precise mechanisms by which it operates remain obscure. This study aimed to investigate alternations in structural connectivity and functional connectivity (SC‐FC) coupling among schizophrenia patients with AVH prior to and following treatment with 1 Hz rTMS that specifically targets the left temporoparietal junction. Initially, patients exhibited significantly reduced macroscopic whole brain level SC‐FC coupling compared to healthy controls. Notably, SC‐FC coupling increased significantly across multiple networks, including the somatomotor, dorsal attention, ventral attention, frontoparietal control, and default mode networks, following rTMS treatment. Significant alternations in SC‐FC coupling were noted in critical nodes comprising the somatomotor network and the default mode network, such as the precentral gyrus and the ventromedial prefrontal cortex, respectively. The alternations in SC‐FC coupling exhibited a correlation with the amelioration of clinical symptom. The results of our study illuminate the intricate relationship between white matter structures and neuronal activity in patients who are receiving low‐frequency rTMS. This advances our understanding of the foundational mechanisms underlying rTMS treatment for AVH.

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Section: Discussionmentioning

confidence: 99%

Low‐frequency rTMS induces modifications in cortical structural connectivity ‐ functional connectivity coupling in schizophrenia patients with auditory verbal hallucinations

Xie,

Li,

Guan

et al. 2024

Human Brain Mapping

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“…Several studies have targeted the temporoparietal junction, generally using an "inhibitory" protocol [293][294][295] , as it represents a core region of overactivity within neural circuits associated with hallucinations 304 . Studies examining post-treatment changes found increased network connectivity in regions of the auditory/sensorimotor, central executive, and default mode networks 305 , and normalized connectivity between the default mode and language networks, and within the auditory and central executive networks 306 . Another protocol using "excitatory" rTMS, with a functionally identified target in the language region of the superior temporal sulcus, observed a decrease in hallucinations 307 .…”

Section: Neurostimulationmentioning

confidence: 99%

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Voineskos,

Hawco,

Neufeld

et al. 2024

World Psychiatry

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Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case‐control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data‐driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case‐control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

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“…As a result, dynamic FC (dFC) analysis has emerged as an increasingly popular topic, which can capture inherent dynamic communication of brain functional networks [3], and has been widely used in various brain diseases, including such as Alzheimer's disease, schizophrenia, and depressive disorder [4]. Schizophrenia is a complex psychiatric disorder that affects cognitive and emotional processes, and aberrant connectivity symptoms among auditory (AUD), executive control and cerebellum have been founded in schizophrenia [5][6][7], and is associated with clinical symptoms, such as AUD hallucinations [8,9]. Several dFC-related investigations also have revealed some significant FC in schizophrenia patients [10,11].…”

Section: Introductionmentioning

confidence: 99%

Dynamic functional network connectivity analysis in schizophrenia based on a spatiotemporal CPD framework

Kuang,

Li,

Zhang

et al. 2024

J. Neural Eng.

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Objective. Dynamic functional network connectivity (dFNC), basedon data-driven group independent component analysis (GICA), is an importantavenue for investigating underlying patterns of certain brain diseases such asschizophrenia. Canonical polyadic decomposition (CPD) of a higher-way dynamicfunctional connectivity tensor, can offer an innovative spatiotemporal frameworkto accurately characterize potential dynamic spatial and temporal fluctuations.Since multi-subject dFNC data from sliding-window analysis are also naturallya higher-order tensor, we propose an innovative sparse and low-rank CPD(SLRCPD) for the three-way dFNC tensor to excavate significant dynamicspatiotemporal aberrant changes in schizophrenia. Approach. The proposedSLRCPD approach imposes two constraints. First, the L1 regularization onspatial modules is applied to extract sparse but significant dynamic connectivityand avoid overfitting the model . Second, low-rank constraint is added on timevarying weights to enhance the temporal state clustering quality. Shared dynamicspatial modules, group-specific dynamic spatial modules and time-varying weightscan be extracted by SLRCPD. The strength of connections within- and betweenICNs and connection contribution are proposed to inspect the spatial modules.K-means clustering and classification are further conducted to explore temporalgroup difference. Main results. 82-subject resting-state fMRI dataset and openingCOBRE schizophrenia dataset both containing schizophrenia patients (SZs) andhealthy controls (HCs) were utilized in our work. Three typical dFNC patternsbetween different brain functional regions were obtained. Compared to the spatialmodules of HCs, the aberrant negative connections among auditory network,somatomotor, visual and cognitive control networks in 82-subject dataset andCOBRE dataset were detected. Four temporal states reveal significant differencebetween SZs and HCs for these two datasets. Additionally, the accuracy valuesfor SZs and HCs classification based on time-varying weights are larger than0.96. Significance. This study excavates significantly spatio-temporal patternsfor schizophrenia disease.

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Impact of low-frequency repetitive transcranial magnetic stimulation on functional network connectivity in schizophrenia patients with auditory verbal hallucinations

Cited by 14 publications

References 101 publications

Low‐frequency rTMS induces modifications in cortical structural connectivity ‐ functional connectivity coupling in schizophrenia patients with auditory verbal hallucinations

Low‐frequency rTMS induces modifications in cortical structural connectivity ‐ functional connectivity coupling in schizophrenia patients with auditory verbal hallucinations

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Dynamic functional network connectivity analysis in schizophrenia based on a spatiotemporal CPD framework

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